Single-cell RNA-seq analysis of human coronary arteries using an enhanced workflow reveals SMC transitions and candidate drug targets

Abstract

Recent advances in single-cell RNA sequencing (scRNA-seq) methods have enabled high-resolution profiling and quantification of cellular expression and transcriptional states. Here we incorporate automated cell labeling, pseudotemporal ordering, ligand-receptor evaluation, and drug-gene interaction analysis into an enhanced and reproducible scRNA-seq analysis workflow. We applied this analysis method to a recently published human coronary artery scRNA dataset and revealed distinct derivations of chondrocyte-like and fibroblast-like cells from smooth muscle cells (SMCs). We highlighted several key ligand-receptor interactions within the atherosclerotic environment through functional expression profiling and revealed several attractive avenues for future pharmacological repurposing. This publicly available workflow will also allow for more systematic and user-friendly analysis of scRNA datasets in other disease systems.